code: def _test_trainer_torch_with_evaluator_fp16_accumulation_steps(
      accumulation_steps, fp16
  ):
      trainer = Trainer(
          model=model,
          driver="torch",
          device=None,
          optimizers=optimizers,
          train_dataloader=train_dataloader,
          evaluate_dataloaders=evaluate_dataloaders,
          metrics=metrics,
          n_epochs=2,
          data_device=local_device,
          progress_bar="rich",
          accumulation_steps=accumulation_steps,
          fp16=fp16,
      )
  
      trainer.run()
  
file_path: fastNLP/tests/core/controllers/_test_distributed_launch_torch_1.py
imports: import argparse
  import os
  import sys
  from dataclasses import dataclass
  from fastNLP.core.controllers.trainer import Trainer
  from fastNLP.envs.imports import _NEED_IMPORT_TORCH
  from tests.helpers.models.torch_model import TorchNormalModel_Classification_2
  from tests.helpers.datasets.torch_data import TorchNormalDataset_Classification
label_desc: Use image node_id 1 for calling a global function with example usage: _test_trainer_torch_with_evaluator_fp16_accumulation_steps(accumulation_steps, fp16) without return types